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Bayesian Statistics 9$
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José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 19 April 2021

Particle Learning for Sequential Bayesian Computation *

Particle Learning for Sequential Bayesian Computation *

(p.317) Particle Learning for Sequential Bayesian Computation*
Bayesian Statistics 9

Hedibert F. Lopes

Michael S. Johannes

Carlos M. Carvalho

Nicholas G. Polson

Oxford University Press

Particle learning provides a simulation‐based approach to sequential Bayesian computation. To sample from a posterior distribution of interest we use an essential state vector together with a predictive distribution and propagation rule to build a resampling‐sampling framework. Predictive inference and sequential Bayes factors are a direct by‐product. Our approach provides a simple yet powerful framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.

Keywords:   Particle Learning, Bayesian, Dynamic Factor Models, Essential state vector, Mixture models, Sequential inference, conditional dynamic linear models, nonparametric, Dirichlet

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